The goal of this proposal is to give a genetic, thermodynamic and structural description of how one protein fold (and function) changes into another. This is based on our ability to: 1) Create highly homologous proteins with different folds (heteromorphic pairs);2) Engineer dual functional capacity within each member of the heteromorphic pair such that one binding function is manifest in one fold and cryptic in the other fold;3) Use the thermodynamic linkage between folding and binding to determine folding propensity;4) Use NMR to determine structures of heteromorphic pairs. Our ability to parse the fold-specific folding code from the overall stability code allows us to study a code for conformational switching between two folds. The specific goals are as follows: 1. Determine an efficient evolutionary path from one fold and function to another;2. Establish the minimum sequence gap separating two functional folds;3. Generate heteromorphic pairs of maximum identity for structural and thermodynamic study. We will study how small sets of amino acids determine two different native folds. This will be done by completely examining the functional sequence space separating the two folds, measuring the effects of small numbers of mutations on folding propensity, and determining structures to assess how a limited set of interactions can determine fold. Understanding how a small set of mutations can cause a switch between two stable, monomeric folds and two different functions will have profound implications on our understanding of protein folding, bioinformatics and the natural evolution of new folds and functions. The aim of this proposal is to better understand how amino acid sequence specifies unique tertiary folds by reducing the folding problem to those amino acids that contain the most information toward specifying one fold versus another. This work has direct implications for understanding the protein folding code and the evolution of new folds and will greatly benefit the fields of protein engineering, protein structure prediction, and de novo protein design. Advances in these areas will be needed in the continuing development of new biomedically useful therapeutics to improve human health.